Cloud-side collaborative data storage and management method for industrial Internet of Things

Abstract

The invention discloses a cloud-side collaborative data storage and management method for an industrial Internet of Things, and relates to the technical field of industrial Internet of Things data management. The method comprises the steps that cloud side long connection is established, and a cloud end and a local data service node establish long connection with a bidirectional authentication and heartbeat mechanism through WebSocket; data hierarchical storage and automatic backup are carried out, a local node is connected with a springboard machine through an SSH2 module and a PEM secret key, cloud data are packaged, downloaded and decompressed, cloud expired data are cleaned according to a strategy of first backup and then deletion, and a three-level storage architecture is formed; according to the seamless query proxy service, the query request of the cloud platform is forwarded to the local through the cloud node, and a result is fed back after local retrieval. Through the design, the problems of high cloud storage cost, difficult offline data access and high data security risk in the prior art are solved, the storage operation and maintenance cost is reduced, the data security is ensured, seamless offline data access is realized, and the method is adaptive to industrial Internet of Things massive equipment scenes.

Description

Technical Field

[0001] This invention belongs to the field of industrial Internet of Things (IoT) data management technology, specifically relating to a cloud-edge collaborative data storage and management method for industrial IoT. Background Technology

[0002] With the rapid development of the Industrial Internet of Things (IIoT), the market logic has shifted from "expanding the number of connected devices" to "systematic mining of data value and deep integration with the industry ecosystem." The management and storage of IoT device data has become a core support for IoT cloud platforms. Currently, mainstream cloud storage solutions mainly include cloud database storage solutions, cloud object storage solutions, cloud NAS storage solutions, and hybrid cloud storage solutions.

[0003] Among them, cloud database storage solutions cover cloud-native relational databases, cloud time-series databases, and cloud NoSQL databases, mainly used to store structured parameters, high-frequency time-series data, and semi-structured data of IoT devices, and are widely used in scenarios such as remote device monitoring and data statistical analysis; cloud object storage solutions, such as Alibaba Cloud OSS, Amazon S3, and Tencent Cloud COS, focus on storing unstructured data such as device log files and configuration backup packages; cloud NAS storage solutions, such as Alibaba Cloud NAS and Huawei Cloud NAS, adopt a file-level storage mode, support standard file protocols such as NFS and CIFS, and enable shared access to data by multiple devices and users; hybrid cloud storage solutions combine the above three solutions, typically using relational databases to store business data, time-series databases to store device time-series data, and object storage to store unstructured data.

[0004] However, existing cloud storage solutions have significant drawbacks: First, they are costly and uncontrollable. Outbound traffic fees from a large number of devices, storage fees that increase exponentially with data volume, and high-frequency read / write request fees constitute the main expenses. As the business scales up, these costs will rise accordingly, creating a huge long-term financial burden. Second, data access is limited. After cloud device data is backed up offline, convenient online access cannot be achieved when the cloud platform business needs to use the offline device data again. Third, data security risks are prominent. Existing solutions rely on the products of operating vendors, and device data cannot be automatically stored and backed up offline, posing a risk of data loss and leakage. Even though some cloud object storage solutions support cloud archiving to reduce costs to some extent, the cost is still higher than offline NAS disk storage, and they also lack automatic offline backup functionality.

[0005] Therefore, there is an urgent need for an industrial IoT data management solution that can reduce cloud storage costs, ensure data security, and enable seamless offline data access to address the shortcomings of existing technologies. Summary of the Invention

[0006] To address the technical challenges of high and uncontrollable costs, inconvenient online access to offline data, and susceptibility to data loss and leakage in existing industrial IoT cloud storage solutions, this application designs a cloud-edge collaborative data storage and management method for industrial IoT. The aim is to achieve hierarchical data storage, automatic backup, secure cleanup, and seamless querying through a cloud-edge collaborative architecture, thereby reducing storage costs while ensuring data security and maintaining the accessibility of historical data.

[0007] A cloud-edge collaborative data storage and management method for the Industrial Internet of Things includes the following steps: Step S1: Establishing a long connection between the cloud and the edge: Establish a long connection between the cloud data service node and the local data service node and maintain communication, wherein the cloud data service node acts as a WebSocket server and the local data service node acts as a WebSocket client; Specifically, the establishment and maintenance of long-lived connections includes the following steps: Step S11: When the local data service node starts and initializes, it creates a WebSocket connection and listens for the connection open / establishment success event ONOPEN, message reception event ONMESSAGE, and connection close / disconnection event ONCLOSE. Step S12: When the ONOPEN event is triggered, the local data service node sends a public identifier key KEY and a private verification key SECRET to the cloud data service node for verification. After successful verification, a heartbeat timer is started and heartbeats are sent periodically to maintain the connection. Step S13: When the ONCLOSE event is triggered, clear the heartbeat timer and reinitialize the WebSocket connection to reconnect with the cloud data service node.

[0008] Preferably, the long connection in step S1 is implemented through a secure communication channel, which adopts a two-way authentication mechanism to avoid server resource consumption and network communication security risks caused by frequently establishing short connections.

[0009] Step S2: Tiered Data Storage and Automatic Backup Local data service nodes execute scheduled tasks to archive and back up cloud device data to the local storage environment, while cleaning up expired cloud data according to preset strategies, forming a multi-level data storage architecture.

[0010] (1) The specific operations for scheduled data backup include: Step S201: The local data service node connects to the jump server that has been mounted on the cloud NAS through a third-party SSH2 module and a PEM key; Step S202: Use the Linux [tar] command to package and compress all device data from the previous day into a compressed file in the format YYYYMMDD.tar.gz; Step S203: Download the compressed file to the local NAS path using the SFTP file transfer protocol based on the SSH2 protocol; Step S204: Decompress the compressed file using the Linux [tar] command to create a device data folder that can be directly accessed by the local data service node.

[0011] (2) The specific operations for regularly cleaning up expired data in the cloud include: Step S211: The local data service node connects to the jump server that has been mounted on the cloud NAS through a third-party SSH2 module and a PEM key; Step S212: Read the cloud platform's preset "Retain online data for the most recent N days" configuration item; Step S213: Determine if the device data folder from N days ago to the present time exists. If it exists, it is determined that the corresponding data has been successfully backed up to the local machine. Delete the folder and the corresponding compressed file using the Linux command

rm -rf

[0012] (3) The multi-level data storage architecture includes three data layers: Online hot data layer: Deployed on a highly available cloud storage medium, it stores device data generated within the last N days, supporting high-frequency, low-latency data access; Near-line temperature data layer: Through timed archiving and compression, device data exceeding N days is migrated to local or private storage environments, supporting low-frequency, traceable data access; Offline cold data layer: Long-term offline storage of historical device data, suitable for application scenarios such as compliance auditing and incident tracing.

[0013] Preferably, in step S2, N is a positive integer in the configuration item "Retain online data for the most recent N days", and the prerequisite for executing the cloud expired data cleanup is "first successfully back up the cloud data to the local storage environment, and then perform the cloud data deletion operation", to ensure that the device data storage is not lost.

[0014] Preferably, the cloud device data is divided and archived by day and stored as a single database file. The file name format is equip_equipid_YYYYMMDD.db, where equipid is the unique identifier of the industrial IoT device and YYYYMMDD is a combination of four-digit year, two-digit month, and two-digit day.

[0015] Step S3, Seamlessly query agent service: After receiving a historical data query request from a device, the cloud platform forwards it to the local data service node through the cloud data service node. The local data service node parses the request and retrieves the data, then sends the results back to the cloud platform through the cloud data service node, thus completing the query loop.

[0016] Seamless query proxy service includes the following roles and processes: Step S31: The cloud data service node provides a data query interface to the cloud platform. Without needing to determine the data storage location, it directly forwards the query request to the local data service node. Step S32: The local data service node parses the query request and verifies the payload, calls the data query API to perform the retrieval operation, and returns the retrieval results to the cloud data service node; Step S33: The cloud data service node will return the received search results to the cloud platform business system as is.

[0017] The advantages and effects of this application are as follows: 1. The cloud-edge collaborative data storage and management method for industrial IoT designed in this application uses a hierarchical data storage architecture to retain frequently accessed recent data (online hot data) in the cloud and migrate infrequently accessed historical data (near-line temperature data, offline cold data) to local or private storage environments. This avoids the exponential growth of cloud storage costs with the amount of data, while reducing the additional overhead caused by cloud data read and write requests and reducing long-term financial burden; thus significantly reducing storage and operation and maintenance costs.

[0018] 2. The cloud-edge collaborative data storage and management method for industrial IoT designed in this application realizes data transmission between the cloud and local through a secure connection method of PEM key + SSH2 module. Combined with the cloud data cleanup strategy of "backup first and then delete", data loss or leakage is avoided. Long connection adopts two-way authentication and heartbeat mechanism to ensure the stability and security of cloud-edge communication and prevent unauthorized node access; thus ensuring data security and integrity.

[0019] 3. The cloud-edge collaborative data storage and management method for industrial IoT designed in this application, through a seamless query proxy service, allows the cloud platform to forward query requests to the local data service node without determining the actual storage location of the data. The local node retrieves the backup data and then returns the results, which solves the problem that offline backup data cannot be easily accessed online in the prior art, ensures the traceability of historical data, and realizes seamless offline data query.

[0020] 4. The cloud-edge collaborative data storage and management method for industrial IoT designed in this application can flexibly adjust the N value (the number of days to retain online data in the cloud) according to business needs through a multi-level data storage architecture, adapting to the data access frequency and storage requirements of different industrial scenarios; the cloud-edge collaborative architecture supports the access of massive industrial IoT devices, can be horizontally expanded as the business scale increases, has long-term evolution capabilities, and improves system scalability and adaptability. The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings.

[0021] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0023] Figure 1 A flowchart of the seamless query proxy service designed for this application; Figure 2 A flowchart for establishing and maintaining cloud edge long connections designed for this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to help fully understand the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. In addition, for clarity and brevity, descriptions of known functions and structures are omitted in the embodiments.

[0025] It should be understood that the phrase "an embodiment" or "this embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "an embodiment" or "this embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0026] Furthermore, reference numerals and / or letters may be repeated in different examples within this application. Such repetition is for the purpose of simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or settings discussed.

[0027] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another type of relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0028] In this article, the term "at least one" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, "at least one of A and B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0029] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion.

[0030] Example 1 In the current YuYang Cloud service platform, all device-related data is initially stored using a cloud-based data storage solution (cloud NAS storage). Device data is archived daily (one folder per day, folder format YYYYMMDD, e.g., 20250101), and each device's data is isolated from others, stored in a single database file (named equip_equipid_YYYYMMDD.db, e.g., equip_996_20250101.db). For data privacy and security reasons, device data can only be accessed via API within the cluster's intranet. Therefore, if offline backup of device data is required, the cloud NAS disk storing the device data is typically mounted to a jump server, which then performs the backup and management of the device data.

[0031] To address the issues of exorbitant costs and difficulties in offline access caused by accessing and storing massive amounts of device data, this patent designs the YuYang Private Data Service (hereinafter referred to as "this Service"), which is used to back up massive amounts of device data generated in the cloud offline and provide an interface service for online data querying of cloud devices. The core of this Service includes two parts: one is an intelligent data hierarchical storage and automatic backup mechanism, and the other is a proxy service that provides seamless querying to the cloud platform.

[0032] This service is divided into two parts: cloud (hereinafter referred to as PDSC_CLOUD) and local (hereinafter referred to as PDSC). PDSC_CLOUD establishes a long connection with PDSC. When there is a data query request in the cloud platform, PDSC_CLOUD forwards the data query request to PDSC. PDSC executes the data query request. At the same time, PDSC executes a scheduled task to periodically store the cloud data to the local machine.

[0033] Please refer to Figure 1 This embodiment is based on the YuYang Cloud service platform. The platform initially adopts a cloud-based NAS storage solution. Device data is archived daily, with each device's data isolated and stored in a single database file named equip_equipid_YYYYMMDD.db (e.g., equip_996_20250101.db). Device data can only be accessed via API within the cluster's intranet. This embodiment achieves cloud-edge collaborative data storage and management by deploying a cloud data service node (PDSC_CLOUD) and a local data service node (PDSC). The specific implementation process is as follows: I. Preparations before implementation: Deploy PDSC_CLOUD (WebSocket Server) in the cloud, configure the listening port (such as port 8080), preset the KEY and SECRET of the valid local node, and enable the two-way authentication mechanism; Deploy PDSC (WebSocket Client) locally, integrate a third-party SSH2 module (such as the ssh2 module of Node.js), and configure the PEM key, jump host IP address, port, cloud NAS mount path and local NAS storage path; The cloud platform is configured with the parameter "Retain online data for the most recent N days". In this embodiment, N is set to 7 (i.e., retain online data from the cloud for the most recent 7 days). The jump server is connected to a cloud NAS and has SSH access enabled, allowing only nodes with valid PEM keys to connect.

[0034] II. For specific implementation steps, please refer to... Figure 2 : Step 1: Establishing and maintaining long-lived cloud-edge connections: 1.1 When PDSC starts up and initializes, it creates a WebSocket connection with the target address being the IP address and port of PDSC_CLOUD (e.g., 192.168.1.100:8080), and listens for the ONOPEN, ONMESSAGE, and ONCLOSE events. 1.2 After the connection channel is established, the ONOPEN event is triggered. The PDSC sends a verification request to PDSC_CLOUD with a preset KEY (such as “device_001_key”) and SECRET (such as “device_001_secret_xxx”). 1.3 After receiving the verification request, PDSC_CLOUD compares the locally registered KEY with the SECRET. If the verification is successful, it returns a confirmation message. After receiving the confirmation message, PDSC starts a heartbeat timer and sends a heartbeat packet (such as the "ping" command) every 30 seconds to maintain a long connection. 1.4 If a network failure or PDSC_CLOUD restart causes the connection to be lost, triggering the ONCLOSE event, PDSC will immediately clear the heartbeat timer, reinitialize the WebSocket connection after a 5-second interval, and repeat steps 1.2-1.3 until the reconnection is successful.

[0035] Step 2: Tiered Data Storage and Automatic Backup 2.1 Scheduled data backup (a scheduled task is executed daily at 2 AM): 2.1.1 PDSC loads the PEM key via the SSH2 module and connects to the jump server (IP: 192.168.1.200, port 22). 2.1.2 Execute the Linux command

tar -zcvf / tmp / 20250101.tar.gz / cloud_nas / 20250101 /

tar -zxvf / local_nas / backup / 20250101 / 20250101.tar.gz -C / local_nas / data / 20250101 /

[0036] 2.2 Cloud-based expired data cleanup (a scheduled task executed daily at 3 AM): 2.2.1 The PDSC reconnects to the jump server using the SSH2 module and the PEM key; 2.2.2 Read the "Retain online data for the last 7 days" parameter configured on the cloud platform. The current date is January 8, 2025. Therefore, it is necessary to determine whether the device data folder ( / cloud_nas / 20250101 / ) from January 1, 2025 (7 days before the current date) exists. 2.2.3. Once the folder is detected, it is determined that the corresponding data has been successfully backed up to the local machine. Execute the Linux command

rm -rf / cloud_nas / 20250101 / / tmp / 20250101.tar.gz

[0037] 2.3 Implementation of hierarchical data storage: Online hot data layer: Device data in the cloud NAS from January 2, 2025 to January 8, 2025, supporting the high-frequency, low-latency data access needs of the cloud platform; Near-line temperature data layer: Archived and decompressed data from January 1, 2025 and earlier in the local NAS, supporting low-frequency, traceable data access needs; Offline cold data layer: migrate historical data (such as data before October 1, 2024) from the local NAS to offline storage devices (such as hard disk arrays) for compliance auditing and incident tracing scenarios.

[0038] Step 3, Seamlessly Inquire About Agent Services: 3.1 The cloud platform business system initiates a historical data query request for the device. The request content is "Query the operating data of the device with device ID 996 on January 1, 2025". This request is sent to the query interface provided by PDSC_CLOUD (such as / api / query / history). 3.2 PDSC_CLOUD does not require determining the data storage location; it directly forwards the query request to PDSC via a WebSocket long connection. 3.3 After PDSC parses the query request and verifies the validity of the payload, it determines that the device ID to be queried is 996 and the date is January 1, 2025. It then calls the local data query API (e.g., / local_api / query?equipid=996&date=20250101) to retrieve the equip_996_20250101.db file under the local NAS / data / 20250101 / path. 3.4. PDSC forwards the retrieved device operation data to PDSC_CLOUD via a WebSocket long connection; 3.5 PDSC_CLOUD feeds the data back to the cloud platform business system as is, completing the query loop. Users do not need to be aware that the data is stored locally, enabling seamless access.

[0039] III. Verification of Implementation Results: After running this embodiment for one month, the statistics are as follows: The amount of data stored in the cloud is reduced by about 85% (only the data of the most recent 7 days is retained), cloud storage costs are reduced by more than 70%, and outbound traffic costs are reduced by 65%, which significantly reduces operating costs; The average response time for historical data queries is 300ms, which is basically the same as the response time for cloud data queries, enabling seamless access to offline data. During the three network fluctuations, PDSC reconnected within 10 seconds each time, without any data transmission interruption or loss, thus verifying data security and system stability.

[0040] The above description is merely a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any changes, modifications, substitutions, integrations, and parameter alterations to these embodiments within the spirit and principles of the present invention, achieved through conventional substitutions or by achieving the same function without departing from the principles and spirit of the present invention, fall within the scope of protection of the present invention.

Claims

1. A cloud-edge collaborative data storage and management method for the Industrial Internet of Things (IIoT), characterized in that, Includes the following steps: Step S1: Establishing a long connection between the cloud data service node and the local data service node and maintain communication. The cloud data service node acts as a WebSocket server and the local data service node acts as a WebSocket client. Step S2, Data Tiered Storage and Automatic Backup: The local data service node executes scheduled tasks to archive and back up cloud device data to the local storage environment, and at the same time cleans up expired cloud data according to preset strategies, forming a multi-level data storage architecture; Step S3, Seamless Query Agent Service: After receiving the device's historical data query request, the cloud platform forwards it to the local data service node through the cloud data service node. The local data service node parses the request and retrieves the data, and then feeds the results back to the cloud platform through the cloud data service node, completing the query loop.

2. The cloud-edge collaborative data storage and management method for industrial IoT according to claim 1, characterized in that, In step S1, the establishment and maintenance of a long connection includes the following steps: Step S11: When the local data service node starts and initializes, it creates a WebSocket connection and listens for the connection open / establishment success event ONOPEN, message reception event ONMESSAGE, and connection close / disconnection event ONCLOSE. Step S12: When the ONOPEN event is triggered, the local data service node sends a public identifier key KEY and a private verification key SECRET to the cloud data service node for verification. After successful verification, a heartbeat timer is started and heartbeats are sent periodically to maintain the connection. Step S13: When the ONCLOSE event is triggered, clear the heartbeat timer and reinitialize the WebSocket connection to reconnect with the cloud data service node.

3. The cloud-edge collaborative data storage and management method for industrial IoT according to claim 1, characterized in that, In step S2, the specific operations for scheduled data backup include: Step S201: The local data service node connects to the jump server that has been mounted on the cloud NAS through a third-party SSH2 module and a PEM key; Step S202: Use the Linux [tar] command to package and compress all device data from the previous day into a compressed file in the format YYYYMMDD.tar.gz; Step S203: Download the compressed file to the local NAS path using the SFTP file transfer protocol based on the SSH2 protocol; Step S204: Decompress the compressed file using the Linux [tar] command to create a device data folder that can be directly accessed by the local data service node.

4. The cloud-edge collaborative data storage and management method for industrial IoT according to claim 1, characterized in that, In step S2, the specific operations for the scheduled cleanup of expired data in the cloud include: Step S211: The local data service node connects to the jump server that has been mounted on the cloud NAS through a third-party SSH2 module and a PEM key; Step S212: Read the cloud platform's preset "Retain online data for the most recent N days" configuration item; Step S213: Determine if the device data folder from N days ago to the present time exists. If it exists, it is determined that the corresponding data has been successfully backed up to the local machine. Delete the folder and the corresponding compressed file using the Linux command 【rm -rf】.

5. The cloud-edge collaborative data storage and management method for industrial IoT according to claim 1, characterized in that, In step S3, the seamless query proxy service includes the following roles and processes: Step S31: The cloud data service node provides a data query interface to the cloud platform. Without needing to determine the data storage location, it directly forwards the query request to the local data service node. Step S32: The local data service node parses the query request and verifies the payload, calls the data query API to perform the retrieval operation, and returns the retrieval results to the cloud data service node; Step S33: The cloud data service node will return the received search results to the cloud platform business system as is.

6. The cloud-edge collaborative data storage and management method for industrial IoT according to claim 1, characterized in that, The multi-level data storage architecture in step S2 includes three data tiers: Online hot data layer: Deployed on a highly available cloud storage medium, it stores device data generated within the last N days, supporting high-frequency, low-latency data access; Near-line temperature data layer: Through timed archiving and compression, device data exceeding N days is migrated to local or private storage environments, supporting low-frequency, traceable data access; Offline cold data layer: Long-term offline storage of historical device data, suitable for application scenarios such as compliance auditing and incident tracing.

7. The cloud-edge collaborative data storage and management method for industrial IoT according to claim 1 or 2, characterized in that, The long connection in step S1 is implemented through a secure communication channel, which adopts a two-way authentication mechanism to avoid server resource consumption and network communication security risks caused by frequently establishing short connections.

8. The cloud-edge collaborative data storage and management method for industrial IoT according to claim 1 or 3, characterized in that, Cloud device data is divided and archived by day and stored as a single database file. The file name format is equip_equipid_YYYYMMDD.db, where equipid is the unique identifier of the industrial IoT device and YYYYMMDD is a combination of four-digit year, two-digit month, and two-digit day.

9. The cloud-edge collaborative data storage and management method for industrial IoT according to claim 1 or 4, characterized in that, In step S2, N is a positive integer in the configuration item "Retain online data from the most recent N days", and the prerequisite for executing the cloud expired data cleanup is "first successfully back up the cloud data to the local storage environment, and then perform the cloud data deletion operation" to ensure that the device data storage is not lost.

10. The cloud-edge collaborative data storage and management method for industrial IoT according to claim 1 or 5, characterized in that, In step S3, when the local data service node parses the query request, if it detects that the request payload is a device historical data query request, it directly calls the local data query API; the search results are forwarded back to the cloud data service node through the WebSocket long connection channel, and the cloud data service node synchronously feeds back to the query interface of the cloud platform.

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